CCDM Certified Clinical Data Manager Questions and Answers

When a clinical study usesblood pressure (BP)as anefficacy endpoint, the most reliable and standardized method of data collection is throughstudy-provisioned equipment.

According to theGCDMP (Chapter: CRF Design and Data Collection), data collected for primary efficacy endpoints must beconsistent, accurate, and standardized across all investigative sites. Usingstudy-provided calibrated equipmentensures that measurements are taken under uniform conditions, eliminating inter-site variability due to differences in devices, calibration, or measurement methods.

Collecting BP data from medical records (option A) risks inconsistent timing and techniques. Using each site’s own equipment (option B) introduces variability, while patient self-reports (option D) lack reliability and objectivity.

Thus, thebest practiceis to provision and standardize all equipment used to collect endpoint-related physiological data, ensuring regulatory-quality results suitable for analysis.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 5.1 – Standardization of Clinical Measurements

ICH E6 (R2) GCP, Section 5.5.3 – Data Accuracy and Equipment Standardization

FDA Guidance for Industry: Electronic Source Data in Clinical Investigations, Section 4.3 – Data Capture and Standardization Requirements

Prior to an audit, theauditeeshould expect to receive anaudit plan or agenda, which outlines thescope, objectives, schedule, and logisticsof the audit.

According to theGCDMP (Chapter: Quality Assurance and Audits), anaudit planensures transparency, preparation, and efficient execution. It typically includes details such as:

The audit scope and objectives,

The audit team members,

Documents or processes to be reviewed, and

The audit schedule and timeframe.

This allows the auditee to prepare the necessary records, staff, and facilities. While the auditor’s credentials (option A) may be shared informally, they are not a regulatory requirement.Corrective actions (option B)are outcomes of the audit, not pre-audit materials.Standard Operating Procedures (option C)may be requested during the audit but are not provided in advance.

Thus,Option D – Audit Plan or Agenda– is the correct and compliant answer.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Quality Assurance and Audits, Section 6.1 – Pre-Audit Planning and Communication

ICH E6 (R2) Good Clinical Practice, Section 5.19.3 – Audit Procedures and Responsibilities

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Section 8.1 – Audit Preparation and Planning

The section of theClinical Study Report (CSR)that is most useful for a Data Manager is the one that includes theenumeration and explanation of data errors. This section provides a summary of thedata quality control findings, including error rates, missing data summaries, and any issues identified during data review, validation, or database lock.

According to theGCDMP (Chapter: Data Quality Assurance and Control), post-study reviews of data errors and quality findings are essential for evaluating process performance, identifying recurring issues, and informing continuous improvement in future studies.

Other sections, such as clinical narratives (A) or statistical methods (C), are outside the core scope of data management responsibilities. Thedata error enumeration sectiondirectly reflects the quality and integrity of the data management process and is therefore the most relevant for review.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Quality Assurance and Control, Section 6.4 – Quality Reporting and Error Analysis

ICH E3 – Structure and Content of Clinical Study Reports, Section 14.3 – Data Quality Evaluation

The most effective information for addressinga backlog of unresolved queriesat investigative sites is alist of late queries by site combined with a summary table.

According to theGCDMP (Chapter: Communication and Issue Escalation), timely and structured feedback to sites is critical for efficient query resolution. A detailed list oflate or overdue queries, accompanied by summary statistics (e.g., counts, durations, status), enables data managers and monitors to prioritize follow-up actions, target problem areas, and provide focused support or retraining to underperforming sites.

While query count summaries (option B) are helpful for overview metrics, they lack the specific information (query ID, date, field, status) required for targeted follow-up. Graphs of enrollment or clean cases (options A and C) are unrelated to discrepancy resolution performance.

Thus, the combination ofdetailed lists and summarized performance metricsoffers both granularity and a high-level overview — the optimal tool for query management communication.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Communication and Issue Escalation, Section 5.1 – Site Query Management Reports

ICH E6 (R2) GCP, Section 5.18.4 – Communication Between Monitors and Sites

FDA Guidance for Industry: Oversight of Clinical Investigations – Risk-Based Monitoring, Section on Query Metrics and Site Performance Review

Before selecting anElectronic Data Capture (EDC)system for a clinical trial, it is essential to have a clear understanding of thefunctional requirements. This serves as theminimum prerequisiteto guide system selection, ensuring that the EDC solution aligns with the protocol needs, data workflow, security requirements, and regulatory compliance.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Computerized Systems and Compliance), functional requirements describe what the system must do—such as data entry capabilities, edit checks, query management, user roles, audit trails, and integration with external systems (e.g., labs, ePRO). This understanding allows sponsors and CROs to evaluate vendor systems effectively during the selection and qualification phase.

Other options:

B. Installation qualificationandD. Validation planoccuraftersystem selection.

C. Governance documentationsupports operations but is not required before choosing the system.

Hence,option Ais correct — the first and most essential prerequisite before EDC selection is a solid understanding of thefunctional requirements.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Computerized Systems and Compliance, Section 4.2 – Requirements Gathering and System Selection

FDA 21 CFR Part 11 – System Validation and Intended Use Requirements

ICH E6(R2) GCP, Section 5.5.3 – Computerized System Selection and Qualification

InElectronic Data Capture (EDC)systems, free text fields should be usedonly when a predefined list of acceptable responses cannot accommodate the full variability of input data— most notably forAdverse Event (AE) verbatim terms.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: CRF Design and Data Collection), AE verbatim terms are initially entered asfree textby site staff to accurately capture the investigator’s exact medical description of the event. These verbatim terms are latercodedusing standardized dictionaries such asMedDRAduring medical coding, ensuring both flexibility and standardization in reporting.

Conversely, fields such asurine sedimentation rate (A),date of birth (C), andBody Mass Index (D)requirestructured numeric or date formatsto enable validation, range checks, and consistency across datasets. Free text would compromise data integrity, accuracy, and validation efficiency for these structured data elements.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 4.3 – Use of Free Text and Coded Fields

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Data Structure and Validation

MedDRA Introductory Guide, Section 2.3 – Verbatim Entry and Coding Requirements

UnderFDA 21 CFR Part 11, organizations using electronic systems must ensure thatall system users are trainedto perform their assigned functions before gaining access to the system. The regulation requires documented evidence of training but does not specify how it should be conducted (e.g., exam-based, in person, or language-specific).

TheGCDMP (Chapter: Computerized Systems and Compliance)further clarifies that personnel training should include instruction onsystem functionality, audit trails, data entry procedures, and electronic signaturesto maintain compliance and data integrity. Training must beperformed and documentedbut does not require a specific format or delivery method.

Therefore,option A—Training must be performed—is correct, as it reflects theminimum regulatory expectationper FDA and SCDM standards.

Reference (CCDM-Verified Sources):

FDA 21 CFR Part 11, Section 11.10(i) – Personnel Training Requirements

SCDM GCDMP, Chapter: Computerized Systems and Compliance, Section 5.4 – System Training and Documentation

ICH E6(R2) GCP, Section 2.8 – Qualified Personnel and Training Requirements

In clinical data management, aderived fieldrefers to any variable that is not directly collected from the Case Report Form (CRF) but is instead calculated or inferred from one or more collected variables (for example, calculating an average blood pressure from multiple readings). Proper documentation of derived fields is essential for ensuringdata traceability, transparency, and compliancewith both FDA and SCDM guidelines.

According to theGood Clinical Data Management Practices (GCDMP, May 2007), all derivations and transformations applied to clinical data must beclearly defined and documentedin metadata such as thedataset definition file (also referred to as data specifications, variable definition tables, or Define.xml files). The derivation algorithm should be explicitly stated in this documentation to allow independent verification, regulatory review, and reproducibility of results.

TheFDA Guidance for Industry (April 2006)on electronic submissions further emphasizes thatderived fields must be supported by comprehensive metadatathat defines the computational method used. This documentation enables the FDA or any regulatory body to audit andreproduce analytical results without ambiguity. Annotating or describing derivations directly on the CRF (as in options A, B, or D) isnot sufficient, as CRFs represent data collection instruments—not analytical documentation.

Therefore, the correct and regulatory-compliant practice isto provide the derivation algorithm for a calculated field within the dataset definition file, aligning with bothFDAandGCDMPexpectations for data integrity and auditability.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Handling and Processing – Derived and Calculated Data Fields, Section 5.3.3

FDA Guidance for Industry: Providing Regulatory Submissions in Electronic Format, April 2006, Section 3.2 on Dataset Documentation Requirements

CDISC Define.xml Implementation Guide – Metadata and Algorithm Documentation for Derived Variables

The section of theClinical Study Report (CSR)most useful for aData Manageris thedescription of how data were processed.

According to theGCDMP (Chapter: Data Quality Assurance and Control), this section details thedata handling methodology— includingdata cleaning, coding, transformation, and derivation procedures— all of which are core responsibilities of data management. Reviewing this section ensures that the data processing methods documented in the CSR align with theData Management Plan (DMP),Data Validation Plan (DVP), anddatabase specifications.

Thestatistical methods section (option A)is primarily for biostatistics, and therationale for study design (option B)pertains to clinical and regulatory affairs.Clinical narratives (option D)are used by medical reviewers, not data managers.

By reviewing how data were processed, the Data Manager verifies that the study data lifecycle—from collection to analysis—was conducted in compliance with regulatory and GCDMP standards.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 6.3 – Documentation of Data Processing in Clinical Study Reports

ICH E3 – Structure and Content of Clinical Study Reports, Section 11.3 – Data Handling and Processing

FDA Guidance for Industry: Clinical Study Reports and Data Submission – Data Traceability and Handling Documentation

In anElectronic Data Capture (EDC)system, even after a data manager completes all manual queries and marks data as "clean," the data may later appearuncleanifthe site (study coordinator)makes subsequent updates in the system after re-reviewing thesource documents.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Electronic Data Capture Systems), site users maintain the authority to modify data entries as long as the system remains open for data entry. TheEDC system audit trailcaptures such changes, which can automatically invalidate prior data reviews, triggering new discrepancies or changing system edit-check statuses.

This situation commonly occurs when the site identifies corrections in the source (e.g., wrong date or lab result) and updates the EDC form accordingly. These post-cleaning changes require additional review cycles to ensure the database reflects accurate and verified information before final lock.

Options B, C, and D are incorrect — CRAs and medical monitors cannot directly change EDC data; they can only raise queries or request updates.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Electronic Data Capture Systems, Section 6.3 – Post-Cleaning Data Changes and Audit Trails

ICH E6 (R2) GCP, Section 5.5.3 – Data Integrity and Change Control

FDA 21 CFR Part 11 – Electronic Records: Change Documentation Requirements

In anophthalmology clinical study, data criticality is determined by how directly a data element affectssafety evaluation,efficacy assessment, andregulatory decision-making. According to theGood Clinical Data Management Practices (GCDMP, Chapter on Data Validation and Cleaning), critical data fields are those that:

Have a direct impact on theprimary and secondary endpoints, or

Are essential forsafety interpretation and adverse event causality assessment.

Among the listed options,Concomitant Medications (Option B)are consideredcritical datafor ophthalmology studies. This is because many ocular treatments and investigational products can interact with systemic or topical medications, potentially affectingocular response,intraocular pressure,corneal healing, orvisual function outcomes. Any inconsistency in concomitant medication data could directly influencesafety conclusionsorefficacy interpretations.

Other options, while important, are less critical for this study type:

Subject Identifier (A)is essential for data traceability and audit purposes but is not directly related to safety or efficacy outcomes.

Weight (C)may be relevant in dose-dependent drug trials but is rarely a pivotal variable in ophthalmology, where local administration (eye drops, intraocular injections) is common.

Medical History (D)provides contextual background but does not have the same immediate impact on endpoint analysis as current concomitant treatments that can confound the therapeutic effect or cause ocular adverse events.

PerGCDMPandICH E6 (R2) GCPguidelines, data validation plans must definecritical data fieldsduring study setup, reflecting therapeutic area–specific priorities. For ophthalmology,concomitant medications, ocular assessments (visual acuity, intraocular pressure, retinal thickness, etc.), and adverse eventsare typically designated as critical fields requiring heightened validation, source verification, and reconciliation accuracy before database lock.

Thus, when QA identifies discrepancies between the CRF and source, theConcomitant Medications field (Option B)is the most critical to address immediately to ensure clinical and regulatory data integrity.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.4 – Critical Data Fields and Data Validation Prioritization

ICH E6 (R2) Good Clinical Practice, Section 5.18 – Monitoring and Source Data Verification

FDA Guidance for Industry: Oversight of Clinical Investigations — A Risk-Based Approach to Monitoring, Section 5.3 – Identification of Critical Data and Processes

SCDM GCDMP Chapter: Data Quality Assurance and Control – Therapeutic Area–Specific Data Criticality Examples (Ophthalmology Studies)

Providing the central laboratory vendor with acomplete subject demographic listingallowsongoing reconciliationbetween the sponsor’s EDC system and the vendor’s laboratory databaseduring study conduct.

TheGCDMP (Chapter: External Data Transfers and Integration)emphasizes thatsubject reconciliationensures that all laboratory data correspond to valid enrolled subjects and visits. Regular reconciliation throughout the study prevents data mismatches, missing results, or misassigned lab reports.

This proactive measure supports timely query resolution and data integrity across systems. Waiting until after database lock (as in option A) would delay corrections and risk inconsistencies. Options B and D address secondary benefits but not theprimary purpose—ongoing subject-level reconciliation.

Thus,option Cis correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: External Data Transfers, Section 4.4 – Reconciliation and Vendor Communication

ICH E6(R2) GCP, Section 5.5.3 – Data Management, Reconciliation, and Integration

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.3 – External Data Management

The best metric to identifymalfunctioning or ineffective edit checksis thecount by edit check of the number of times the check fired. This allows data managers to assess whether specific edit checks are performing as intended.

According to theGCDMP, Chapter: Data Validation and Cleaning, edit checks are programmed logic conditions that identify data inconsistencies or potential errors during data entry. A properly functioning edit check should trigger only when data falls outside acceptable or logical limits. If an edit check fires too frequently or not at all, it may indicate alogic errorin the check’s programming or configuration.

By analyzing counts by individual edit checks, data managers can:

Identify checks that never trigger (potentially inactive or incorrectly written),

Detect overactive checks (poorly designed parameters causing excessive false positives), and

Optimize system performance and review efficiency.

This metric supports continuous improvement in data validation logic and contributes to cleaner, higher-quality clinical databases.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.2 – Edit Check Design and Performance Metrics

FDA Guidance: Computerized Systems Used in Clinical Investigations – Section on Validation of Electronic Data Systems

This event qualifies as aSerious Adverse Event (SAE)because itresulted in a prolonged hospitalization, even though the episode itself was mild.

According toICH E2AandGCDMP (Chapter: Safety Data Handling and Reconciliation), an adverse event is considered“serious”if it results in any of the following outcomes:

Death,

Life-threatening situation,

Hospitalization or prolongation of existing hospitalization,

Persistent or significant disability/incapacity, or

Congenital anomaly/birth defect.

The severity (mild, moderate, severe) describesintensity, while seriousness describesregulatory significance and medical outcome. Thus, a mild tachycardia episode leading to extended hospital stay meets theregulatory definition of an SAE.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Safety Data Handling and Reconciliation, Section 5.2 – Definition and Classification of Serious Adverse Events

ICH E2A – Clinical Safety Data Management: Definitions and Standards for Expedited Reporting, Section II – Seriousness Criteria

FDA 21 CFR 312.32 – IND Safety Reporting: Serious Adverse Event Definitions

TheInformed Consent Form (ICF)is the document that explicitly describes what study subjects can expect regardingdata disclosure, privacy, and confidentialityduring and after participation in a clinical trial. According toICH E6 (R2) Good Clinical PracticeandFDA Human Subject Protection Regulations (21 CFR Parts 50 and 56), participants must be fully informed about how their personal and clinical data will be collected, used, stored, and shared — both during the study and in any subsequent data-sharing or publication activities.

TheGCDMPreiterates that clinical data managers must ensure that all data handling practices align with the privacy commitments made in the ICF. This includes compliance withdata protection regulationssuch as HIPAA (in the U.S.) and GDPR (in the EU). The ICF defines the permissible scope of data use, ensuring ethical management and subject protection.

Documents like theprotocolordata sharing planmay outline procedures and responsibilities but do not directly inform participants of their rights and data use expectations. Only theICFis designed for that ethical communication purpose.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Ethics, Privacy, and Data Security

ICH E6 (R2) Good Clinical Practice, Sections 4.8.10 & 4.8.12

FDA 21 CFR Part 50 – Protection of Human Subjects, Informed Consent Requirements

During a regulatory inspection, inspectors expect to find documentedStandard Operating Procedures (SOPs)governing the use, validation, and maintenance ofcomputerized systems, includingdata backup and recovery procedures.

According to theGCDMP (Chapter: Computerized Systems and Compliance)andFDA 21 CFR Part 11, organizations must maintain an SOP that ensuresdata protection against loss, corruption, or unauthorized access. The SOP should describe backup frequency, secure storage, verification of backup integrity, and procedures for data restoration.

While theData Management Plan (A)andEdit Specifications (D)are study-level documents, and theStatistical Analysis Plan (C)focuses on analysis procedures,only a Data Backup Plan (B)constitutes a requiredsystem-level SOPensuring compliance and data continuity.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Computerized Systems and Compliance, Section 5.2 – Data Security, Backup, and Recovery SOPs

FDA 21 CFR Part 11 – Subpart B, Controls for Closed Systems

ICH E6(R2) GCP, Section 5.5.3 – System Security, Data Backup, and Recovery Requirements

In studies utilizingElectronic Data Capture (EDC)systems, many traditional paper-based processes such astracking and retrieving CRFsare eliminated or automated. However,query management and resolutionremain essential because discrepancies, missing data, and protocol deviations still require clarification and correction, regardless of the data collection medium.

According to theGCDMP (Chapter: Data Validation and Cleaning),data queriesare generated automatically or manually when inconsistencies are detected by edit checks. Sites must still respond to these queries electronically to ensure the integrity and completeness of data.

AandDare obsolete with EDC (no physical CRFs).

Brefers to manual data entry updates, which are replaced by direct EDC entry.

C (Resolving queries)continues as a key part of the data management workflow, even in fully electronic environments.

Thus,option Cis correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 5.4 – Query Generation and Resolution in EDC Systems

ICH E6(R2) GCP, Section 5.5.3 – Data Review and Query Resolution Requirements

FDA 21 CFR Part 11 – Electronic Records: Audit Trails and Query Documentation

C

Thekey deliverables for User Acceptance Testing (UAT)are theTest Plan, Test Scripts, and Test Results.

According to theGCDMP (Chapter: Database Design and Validation), UAT is the final validation step before a clinical database is released for production. It confirms that the system performs according to user requirements and protocol specifications.

The deliverables include:

UAT Test Plan:Defines testing objectives, scope, acceptance criteria, and responsibilities.

UAT Test Scripts:Provide step-by-step instructions for testing database functionality, edit checks, and workflows.

UAT Test Results:Document actual test outcomes versus expected outcomes, including any deviations and their resolutions.

These deliverables form part of the system validation documentation required underFDA 21 CFR Part 11andICH E6 (R2)to demonstrate that the database has been properly validated.

Project Plans (option A) and Training (option B) occur in earlier phases, while eCRF Completion Guidelines (option D) support site data entry, not system validation.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Design and Validation, Section 5.3 – User Acceptance Testing Deliverables

FDA 21 CFR Part 11 – Validation Documentation Requirements

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – System Validation Records

Theprimary purpose of system validationin clinical data management is todemonstrate and document that the computerized system performs as intended—accurately, reliably, and consistently—throughout its lifecycle.

According to theGood Clinical Data Management Practices (GCDMP, Chapter on System Validation)andFDA 21 CFR Part 11, validation ensures that all system functions (e.g., data entry, edit checks, audit trails, security) work as designed, providing data integrity, traceability, and regulatory compliance. The focus is onfitness for intended use, meaning the system reliably produces correct and reproducible results in the context of its operational environment.

While meeting regulatory requirements (option C) and fulfilling a validation plan (option B) are components of the process, they arenot the ultimate purpose. The essential goal is ensuring that the system performs as intended, maintainingaccuracy and data integrityfor clinical trial operations.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Computerized Systems and System Validation, Section 5.2 – Purpose and Scope of System Validation

FDA 21 CFR Part 11 – Validation of Computerized Systems for Intended Use

ICH E6(R2) GCP, Section 5.5.3 – Computerized System Validation and Data Integrity

Once theCase Report Form (CRF)has been finalized and database development has begun, thenext primary responsibility of the Data Manageris to prepare aData Validation Plan (DVP)for the clinical database.

According to theGCDMP (Chapter: Database Design and Build), the DVP documents all planned validation procedures — includingedit checks, cross-form validations, discrepancy management workflows, and system testing requirements. This ensures that data entry, processing, and cleaning are consistent with protocol requirements and that the database will produce reliable, auditable data for analysis.

Whilesystem requirement specifications (option B)are prepared before database development begins, andtimeline planning (option C)occurs during the study startup phase, theDVPis the critical next step post-CRF approval to define and validate system logic before user acceptance testing (UAT).

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Design and Build, Section 6.4 – Data Validation Plan (DVP) Development

ICH E6 (R2) GCP, Section 5.5.3 – Validation of Computerized Systems

FDA 21 CFR Part 11 – System Validation Requirements for Electronic Records

Inpaper-based trials, site staff (e.g., study coordinators) record data manually on paper Case Report Forms (CRFs), which are later transcribed by data entry personnel into an electronic database.

However, inEDC-based studies, thesite coordinatoris directly responsible forentering data into the EDC system. This eliminates the need for centralized double data entry and shortens data cleaning timelines.

TheGCDMP (Chapter: Electronic Data Capture Systems)states that EDC systems shift certain tasks, includingdata entry, initial query response, and source verification preparation, to the site level. Yet,data entryremains the most significant additional responsibility compared to paper-based studies.

Option A (Query resolution)is performed in both EDC and paper-based systems.

Option C (Data curation)is typically a Data Management function.

Option D (Medical record abstraction)is part of source documentation, not specific to EDC.

Thus,option B (Data entry)is correct — it is the additional site coordinator duty unique to EDC environments.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Electronic Data Capture (EDC) Systems, Section 5.3 – Site Responsibilities and Workflow Changes

ICH E6(R2) GCP, Section 5.5.3 – Data Entry and Role Delegation in Computerized Systems

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.2 – Site-Level Data Entry Controls

InElectronic Data Capture (EDC) systems, user roles are defined based on thefunctions and permissionsrequired for specific study tasks. The most fundamental and universally applicable roles areData Entry(performed by site staff) andData Review(performed by monitors or data managers).

According to theGCDMP (Chapter: Technology and Electronic Data Capture Systems), defining user roles involves:

Assigningfunctional access levels(e.g., entry, review, query resolution).

Ensuringrole-based securityto protect data integrity.

Complying with21 CFR Part 11andICH E6(R2)access control standards.

OptionsB,C, andDinclude functions (protocol review, analysis programming) not directly controlled within an EDC system.

Thus,option A (Data Entry and Data Review)correctly represents the two core factors considered when defining user roles.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Technology and Electronic Data Capture Systems, Section 4.3 – User Access, Roles, and Permissions

ICH E6(R2) GCP, Section 5.5.3 – System Access and Security Controls

FDA 21 CFR Part 11 – Access Control and Audit Trail Requirements

Standard Operating Procedures (SOPs)are formal, controlled documents that definestandardized processesto ensure clinical trials are conducted in compliance withGood Clinical Practice (GCP), the study protocol, and regulatory requirements (such as ICH and FDA).

According toGood Clinical Data Management Practices (GCDMP)andICH E6(R2) GCP, SOPs are fundamental to quality management systems. They describehowtasks are performed, ensuring consistency, accountability, and traceability across all studies and team members. Proper adherence to SOPs guarantees that data areaccurately generated, documented, and reportedin compliance with ethical and regulatory standards.

Other options serve different purposes:

SAP (B)defines statistical methodology, not compliance control.

DMP (C)focuses on study-specific data handling, not organizational compliance.

CRFs (D)are tools for data collection but do not enforce compliance by themselves.

Therefore,option A (SOP)is correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Quality Management and Compliance, Section 5.1 – Role of SOPs in Regulatory Compliance

ICH E6(R2) GCP, Section 2.13 and 5.1.1 – Quality Management Systems and SOP Requirements

FDA 21 CFR Part 312.50 – Sponsor Responsibilities and Compliance Systems

The efficacy endpoint ofall-cause mortality at one yeardirectly depends on thedate of deathfor each subject, makingOption D – Date of deaththe required data element.

According to theGCDMP (Chapter: Clinical Trial Protocols and Data Planning)andICH E3/E9 Guidelines, the primary efficacy analysis must be based on time-to-event data, particularly when the endpoint involvesmortality or survival. Thedate of deathallows accurate calculation oftime from randomization to event, essential for survival analysis (e.g., Kaplan-Meier curves).

Whilecause of death (C)may be collected for safety or secondary analyses,all-cause mortalityspecifically includes any death regardless of cause.Drug levels (A)andcoagulation times (B)may serve as pharmacodynamic or exploratory endpoints but do not directly measure mortality.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Planning and Protocol Review, Section 5.4 – Defining Data Required for Endpoints

ICH E9 – Statistical Principles for Clinical Trials, Section 2.3 – Time-to-Event Endpoints

FDA Guidance for Industry: Clinical Trial Endpoints for Drug Development and Approval

In aCRF-to-database quality control (QC) audit, auditors compare data recorded on the paper Case Report Form (CRF) with data entered in the electronic database. If discrepancies exist thatcannot be explained by documented data handling conventions, they are classified asaudit findings.

PerGCDMP (Chapter: Data Quality Assurance and Control),data handling conventionsdefine acceptable data entry practices, transcription rules, and allowable transformations. These conventions ensure that CRF data are consistently interpreted and entered.

If a discrepancy deviates from these established rules, it indicates a process gap or error in data entry, validation, or training. Discrepancies justified by protocol design or CRF guidelines would not constitute findings.

Therefore,option C (Discrepancy not explained by the data handling conventions)correctly identifies the criterion for a true QC audit finding.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Quality Assurance and Control, Section 6.1 – Data Handling Conventions and QC Auditing

ICH E6(R2) GCP, Section 5.1 – Quality Management and Documentation of Deviations

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.5 – Data Verification and Audit Findings

Whenmerging data from different clinical trials, theuse of a common adverse event (AE) dictionary(such asMedDRAorWHO Drug) is essential to ensure consistency and comparability across datasets.

According to theGCDMP (Chapter: Standards and Data Mapping)andCDISC SDTM Implementation Guide, data integration across studies requires standardized terminology for adverse events, medications, and clinical outcomes. Using the same AE dictionary ensures that similar terms are coded consistently, allowing accurate cross-study analysis, pooled summaries, and safety reporting.

A sharedsoftware platform (option A)is not necessary if data are mapped to standard formats (e.g., CDISC SDTM). Patient population similarity (option B) affects interpretation but not technical data merging. Study design differences (option C) may influence statistical analysis but not data integration mechanics.

Therefore,Option D – Use of a common adverse event dictionary– is the correct and most critical action for consistent multi-study data integration.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Standards and Data Mapping, Section 5.1 – Use of Standardized Coding Dictionaries

CDISC SDTM Implementation Guide, Section 4.3 – Controlled Terminology and Cross-Study Integration

ICH E3 and E2B – Clinical Data Standards and Safety Coding Requirements

Whenlaboratory dataare missing from a paper-based clinical study, theData Managershould directdata-entry personnel to issue a queryto the investigative site for clarification or correction.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Data Validation and Cleaning), every missing, inconsistent, or out-of-range data point must be reviewed and, if necessary, resolved through the formalquery management process. This ensures that all discrepancies between the source documents and database entries are properly documented, traceable, and auditable.

Data-entry staff arenot authorizedto infer or fill in missing information. They must escalate such discrepancies to the site via query, preservingdata integrityandregulatory compliancewithICH E6 (R2)andFDA 21 CFR Part 11. Calling the patient directly (option B) would violate confidentiality and site communication protocol, while simply flagging or ignoring the issue (options A and D) would not meet GCDMP query resolution standards.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 5.2 – Query Management and Resolution

ICH E6 (R2) Good Clinical Practice, Section 5.18.4 – Communication of Data Discrepancies

FDA 21 CFR Part 11 – Electronic Records; Query Audit Trails Requirements

Regulatory agencies such as theFDAandICHrequire thatelectronic data be retained in a format that preserves audit trails and traceability.

While PDF images (option C) provide a static representation of data, they do not preserve theunderlying audit trail(i.e., who changed what, when, and why). TheASCII data files with corresponding audit trails(option D) provide complete transparency and comply with21 CFR Part 11andGCDMParchival standards.

Option A(storing computers) is unnecessary and impractical, andOption B(paper source documents) are site records, not system archives.

Hence,option Dis correct —ASCII data files with audit trailsmeet traceability and compliance standards.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Lock and Archiving, Section 5.4 – Archival Formats and Audit Trail Retention

ICH E6(R2) GCP, Section 5.5.3 – Data Integrity, Audit Trails, and Record Retention

FDA 21 CFR Part 11 – Electronic Records; Audit Trail and Retention Requirements

When existingSDTM (Study Data Tabulation Model)domains do not cover specific efficacy data, thebest practiceis to firstsearch for relevant data element standardsthat may be available throughCDISC CDASH (Clinical Data Acquisition Standards Harmonization)or other recognized industry standards.

PerGCDMP (Chapter: Standards and Data Integration), Data Managers must ensure that new CRF elements are consistent withstandardized definitions, controlled terminology, and data models to support interoperability, future analysis, and regulatory submission.

If no existing standards exist, only then should the Data Manager collaborate with the study team to define new elements — but standard searches always come first.

Thus,option Cis correct —search for relevant data element standardsensures alignment with CDISC best practices and regulatory expectations.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Standards and Data Integration, Section 5.1 – Use of CDISC Standards in CRF Design

CDISC CDASH Implementation Guide, Section 4.1 – Standardization of Data Collection Fields

FDA Study Data Technical Conformance Guide (SDTCG), Section 2.4 – Use of Standard and Custom Domains

In a relational database system used in clinical data management, performance refers to how efficiently the system processes transactions, retrieves data, and handles large volumes of information without delay or data integrity issues. Among the listed options, loading a large lab data file into the database (Option B) has the most significant impact on database performance.

According to theGood Clinical Data Management Practices (GCDMP, Chapter on Database Design and Build), the bulk data load process — such as importing large external datasets (e.g., central lab data, ECG results, or imaging metadata) — can be computationally intensive. This process engages the database’s input/output (I/O) subsystem, indexing mechanisms, and transaction logs simultaneously, often locking tables temporarily and consuming significant memory and processing resources.

Unlike standard CRF data entry (Option A) or record updates (Option D), which are incremental and typically processed in smaller transactional batches, bulk loading operations handle thousands or millions of rows at once. If not optimized (e.g., via staging tables, indexing strategies, or commit frequency control), such operations can degrade system performance, slow down concurrent user access, and increase the risk of transaction failure.

Executing a properly designed query (Option C) can also be resource-intensive depending on data volume and join complexity, but when queries are properly optimized (using indexed keys, efficient SQL joins, and selective retrieval), their impact is generally controlled and transient compared to large data imports.

Therefore, as outlined in theGCDMP Database Design and BuildandFDA Computerized Systems Guidance, the most performance-impacting activity in a relational database is bulk loading large external datasets, making Option B the correct answer.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Database Design and Build, Section 6.7 – Database Performance and Optimization

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6 – System Performance and Data Handling Efficiency

ICH E6 (R2) Good Clinical Practice, Section 5.5 – Data Handling and Record Integrity

CDISC Operational Data Model (ODM) Implementation Guide – Bulk Data Transfer and Validation Considerations

The best choice for managingStandard Operating Procedures (SOPs)in a compliant and auditable manner is aDocument Management System (DMS).

According to theGCDMP (Chapter: Regulatory Requirements and Compliance)andICH E6 (R2), SOPs must beversion-controlled, securely stored, retrievable, and auditable. Avalidated DMSsupports controlled access, document lifecycle management (draft, review, approval, and archival), and electronic audit trails, ensuring full compliance withFDA 21 CFR Part 11andGood Documentation Practices (GDP).

WhileLearning Management Systems (C)track training, they are not intended for document control.Spreadsheets (B)andpaper systems (D)cannot provide adequate version tracking, access security, or audit capability required for regulatory inspection readiness.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Regulatory Requirements and Compliance, Section 5.2 – SOP Management and Document Control

ICH E6 (R2) GCP, Section 5.5.3 – Document and Record Management

FDA 21 CFR Part 11 – Electronic Records and Signatures, Section 11.10 – System Validation and Document Controls

TheData Manager (DM)plays a critical role in maintaining transparent communication with the clinical study team regardingdata quality and study progress. One of the most essential metrics regularly reported by the DM is thepercentage of data entered and cleaned.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Communication and Study Reporting), these metrics provide insight into study status, data readiness for interim analysis, and timeline predictability for database lock. Regular communication includes:

Percent of CRFs entered and verified

Percent of queries resolved

Outstanding data issues or missing pages

Other options fall outside the Data Manager’s direct responsibility:

A (Enrollment)is typically reported by clinical operations.

B (Staffing changes)are handled by site management.

D (Safety events)are communicated by the safety/pharmacovigilance team.

Thus,option Ccorrectly reflects the Data Manager’s responsibility for ongoing study communication.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Communication and Study Reporting, Section 5.3 – Study Metrics and Status Updates

ICH E6(R2) GCP, Section 5.1.1 – Communication and Oversight in Quality Management

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.5 – Data Status Reporting

Electronic submission standards, as established byFDA, CDISC, and ICH, require that an individual subject’scomplete Case Report Form (CRF)be submitted as aPortable Document Format (.pdf)file. ThePDF formatis universally recognized and accepted because it ensures that the structure, format, and visual fidelity of the CRF are preserved exactly as originally designed, regardless of software or hardware environment.

According to theFDA Guidance for Industry: Providing Regulatory Submissions in Electronic Format (2006)andCDISC SDTM standards, sponsors must include asubject-level CRFin PDF form for each participant in the submission dataset. This requirement ensures that reviewers can trace data points from analysis datasets back to their source entries in the CRF, fulfilling the principles ofdata traceability and transparency.

TheGood Clinical Data Management Practices (GCDMP)also support this requirement, emphasizing that CRF archiving should maintain readability and regulatory accessibility. Formats like RTF, DOCX, or SAS datasets are not acceptable substitutes for regulatory CRF submission because they may alter formatting, structure, or introduce modifiable content, violating FDA data integrity principles.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Archiving and Submission

FDA Guidance for Industry: Providing Regulatory Submissions in Electronic Format, April 2006

CDISC SDTM Implementation Guide, Section 5.3 – CRF Representation and Traceability

To properly associatebody-composition data(from a DEXA scanner) with other study data, both thesubject numberand thevisit numberare required.

According to theGCDMP (Chapter: Data Management Planning and Study Start-up), every clinical data record must beuniquely identifiable and linkableto a specific subject and study event. Thesubject numberidentifies the participant, while thevisit numberdefines the temporal context in which the measurement was taken.

Without both identifiers, data integration becomes ambiguous—especially if multiple assessments occur over time (e.g., baseline, week 12, end of study). Including both ensuresdata traceability, integrity, and alignmentwith the protocol-defined schedule of events.

Study number (option A) alone does not distinguish between visits or subjects, and visit number alone (option C) lacks linkage to the individual participant.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Planning and Study Start-up, Section 4.4 – Data Linking and Identification Requirements

ICH E6 (R2) GCP, Section 5.5.3 – Data Traceability Principles

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Data Identification Requirements

When designing or configuringdata entry screenswithin an Electronic Data Capture (EDC) system, three critical components are required for each field:

Data Type– Defines the nature of the data (e.g., text, numeric, date).

Prompt– The label or question displayed to the user.

Response Format– Specifies how the user enters or selects data (e.g., free text, drop-down, checkbox).

According to theGCDMP (Chapter: EDC Systems and Database Design), these three attributes form thelogical data structurerequired to build and validate data entry interfaces. They ensure consistency in how information is captured, displayed, and validated during data entry.

Whileuser roles (A)andhelp text (D)are system-level configurations, not field-level specifications,page numbers (C)relate to printed CRFs rather than digital data screens.

Therefore,option B (Data type, prompt, and response format)correctly identifies the essential information needed to define data entry screens.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: EDC Systems and Database Design, Section 4.3 – Screen Design Specifications

CDISC CDASH Implementation Guide, Section 3.2 – Data Field Attributes

ICH E6(R2) GCP, Section 5.5.3 – Data Capture and Input Standards

AData Transfer Agreement (DTA)defines the operational and technical details for transferring data between a sponsor and an external vendor (e.g., central lab, ECG vendor). It is a formalized, controlled document specifyingwhat data will be sent, when transfers will occur, the transfer method, file structure, encryption or security protocols, and the recipients of the data.

The DTA is developed jointly by the sponsor and vendor before production data transfers begin. According to theGCDMP, Chapter on External Data Transfers, this agreement ensures both parties share a clear understanding of timing, responsibility, and data content to minimize errors and ensure regulatory compliance.

TheData Management Plan (DMP)outlines general data handling processes but does not capture the technical specifics of vendor data transfer logistics. TheProject Plan (A)andCommunication Plan (B)are broader operational tools and not specific to data transfer protocols.

Hence,option C (Data Transfer Agreement)is the correct answer, as it precisely governs the procedural and technical framework of vendor data exchange.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: External Data Transfers, Section 4.1 – Data Transfer Agreements and Specifications

ICH E6(R2) Good Clinical Practice, Section 5.5 – Trial Management, Data Handling, and Record Keeping

When subjects are numberedsequentially within each site, it means that thesubject identification numbers (Subject IDs)restart from 001 at each site. For example, Site 101 may have Subject 001, and Site 102 may also have a Subject 001. In such cases, thesubject number alone is not globally uniqueacross the entire study. Therefore, when integrating or joining data across multiple database tables (for example, linking demographic, adverse event, and laboratory data), both thesite number and the subject numberare required to create a unique key that accurately identifies each record.

According to theGood Clinical Data Management Practices (GCDMP, Chapter on CRF Design and Data Collection), every data record in a clinical trial database must be uniquely and unambiguously identified. This is typically achieved through acomposite key, combining identifiers such assite number,subject number, and sometimesstudy number. The GCDMP specifies that a robust data structure must prevent duplication or mislinking of records across domains or tables.

Furthermore,FDA and CDISC standards (SDTM model)also emphasize the importance ofunique subject identifiers (USUBJID), which are derived from concatenating the study ID, site ID, and subject ID. This ensures traceability, integrity, and accuracy of subject-level data during database joins, data exports, and regulatory submissions.

Thus, in the described scenario, since subject numbering restarts at each site,both the site number and subject numberare required to uniquely identify and correctly join subject data across different datasets or tables.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 4.1 – Unique Subject Identification

CDISC SDTM Implementation Guide, Section 5.2 – Subject and Site Identification (Variable: USUBJID)

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6 – Data Integrity and Record Identification

Even when allrangeandlogic checksare successfully resolved, discrepancies may still exist between theclinical databaseand thesource documents. This typically indicates anerror in data abstraction or transcription, meaning that data were incorrectly entered or extracted from the source records during the data entry or verification process.

According to theGood Clinical Data Management Practices (GCDMP, Chapter on Data Validation and Cleaning),data validation rulessuch as range and logic checks are designed to identify inconsistencies, missing data, or out-of-range valueswithin the databaseitself. However, they donot verify the accuracy of data entry against the original source documents— that responsibility falls undersource data verification (SDV), typically conducted by clinical monitors or auditors.

When an auditor detects discrepancies between source and database values after all edit checks have passed, the most probable explanation is thatdata were not transcribed correctly from the source, rather than a failure in programmed edit checks. This could occur due to human error during manual data entry, misinterpretation of the source document, or oversight during SDV.

OptionC (Data were changed after checks were run)might occur in rare cases but would normally be documented in an audit trail per21 CFR Part 11andICH E6 (R2)standards. OptionBmisinterprets the issue, since “logical and in range” values can still be incorrect relative to the source. OptionA (Auditor error)is possible but statistically less likely, as source data verification follows strict, documented audit procedures.

Therefore, themost likely reasonfor such discrepancies isOption D: Data were not abstracted correctly from the source, emphasizing the importance of robust data entry training, dual data entry, and verification procedures.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.5 – Source Data Verification and Reconciliation

ICH E6 (R2) Good Clinical Practice, Section 5.18 – Monitoring and Source Data Verification

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6 – Source Data Accuracy and Audit Trails

21 CFR Part 11 – Electronic Records and Electronic Signatures, Subpart B: Audit Trails and Record Accuracy

The primary purpose of maintaining anaudit trailas required under21 CFR Part 11isto preserve data integrity. According to the U.S. FDA’s regulation on electronic records and signatures, every change to electronic data must be traceable, including information about who made the change, when it was made, and what the change entailed.

TheGood Clinical Data Management Practices (GCDMP)outlines that an audit trail provides a permanent, chronological record of all modifications to clinical data. This ensures transparency and allows the reconstruction of the course of data entry and modification. The regulation aims to prevent unauthorized or undocumented data manipulation, thereby maintainingthe accuracy, reliability, and validityof electronic records.

TheFDA 21 CFR Part 11, Section 11.10(e)explicitly mandates that systems must usesecure, computer-generated, time-stamped audit trailsto independently record the date and time of operator entries and actions that create, modify, or delete electronic records. This ensures the data remains trustworthy and defensible in regulatory reviews or inspections.

Therefore, the main reason for requiring an audit trail isto preserve data integrity— ensuring that all data captured, modified, or transmitted is authentic, accurate, and complete throughout the study lifecycle.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Regulatory Compliance and Data Integrity

FDA 21 CFR Part 11 – Electronic Records; Electronic Signatures, Section 11.10(e)

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Data Integrity and System Validation

Manual or visual data reviewis used to identifycomplex clinical relationships and contextual inconsistenciesthat cannot be detected by automated edit checks.

According to theGCDMP (Chapter: Data Validation and Cleaning), automated edit checks are ideal for structured validations, such as missing fields (option C), reference ranges (option D), or predefined value lists (option A). However, certain clinical cross-checks—such as verifyingadverse event treatments against concomitant medication records—requireclinical judgmentandcontextual understanding.

For example, if an adverse event of "severe headache" was reported but no analgesic appears in the concomitant medication log, the data may warrant manual review and query generation. These context-based checks are best performed by trained data reviewers or medical data managers during manual data review cycles.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.3 – Manual Review and Clinical Data Consistency Checks

ICH E6 (R2) Good Clinical Practice, Section 5.18.4 – Clinical Data Review Responsibilities

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Data Verification Principles

When a company transitions from paper-based data capture toElectronic Data Capture (EDC)systems, one of the most critical areas requiring procedural updates is theData Review and Validation SOP. The introduction of EDC systems fundamentally changes how data is collected, reviewed, validated, and queried.

According to theGood Clinical Data Management Practices (GCDMP), the implementation of EDC introduces real-time data entry and review, automated edit checks, and electronic query management. These functionalities necessitate revised procedures to define how data validation, discrepancy management, and monitoring are conducted electronically. The SOP must specify roles, responsibilities, system access controls, and processes for electronic source verification (eSource), ensuring compliance with21 CFR Part 11andICH E6 (R2)requirements.

Other SOPs such asHandling External DataorData Backupmay require minor updates, but theData Review and Validation SOPundergoes the most extensive change because EDC technology shifts validation responsibilities from post-data entry review to real-time oversight within the system.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Electronic Data Capture (EDC) Systems, Section 6.3 – SOP Adaptation for EDC Implementation

FDA 21 CFR Part 11 – Electronic Records; Electronic Signatures

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Data Handling and Validation

Before implementing anEDC system upgrade, thefirst taskof the Data Manager is toassess the impact on the data.

According to theGCDMP (Chapter: Electronic Data Capture Systems)andFDA 21 CFR Part 11, any system upgrade must undergoimpact assessmentto determine how the change might affectdata integrity, functionality, validation, and ongoing study operations. This assessment ensures that no data are lost, corrupted, or rendered inconsistent during or after the upgrade.

The Data Manager should evaluate:

Potential effects on existing data, edit checks, and reports,

System functionality impacting current workflows, and

Any revalidation requirements.

Only after the impact is understood should the Data Manager proceed to communicate with sites (option A), update documentation (option B), or modify CRFs if required (option D).

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Electronic Data Capture Systems, Section 7.3 – System Upgrades and Change Control

FDA 21 CFR Part 11 – Change Control and Validation Requirements

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Change Impact on Data Integrity and System Validation